A user's mobility pattern reflects personal location-related interests. Based on the user's mobility pattern, related information can be provided to the user including traffic information, for example.
Currently, user mobility pattern mining is based mainly on historical location data for the user, resulting in a user mobility pattern about locations. However, this would bring the following problems.
First, existing methods focus on mining mobility patterns about locations, but cannot mine topics of a series of related locations (i.e., mobility patterns about topics cannot be obtained). For example, “user goes to the mall after work recently” is a mobility pattern about location and “house decoration” may be a topic for this mobility pattern. The user may visit many different locations for decorating house and some locations may be visited once or several times. Existing methods depend on frequency of visited locations, so they cannot find out such mobility patterns about the topic “house decoration”.
Second, existing methods for mobility pattern mining cannot recognize new mobility patterns early because they require related movements to achieve a certain number of times. However, recognizing new mobility patterns early can help to improve the quality of location-based services.
Thus, there is a need for a solution capable of obtaining user mobility patterns about topics early.